Search results for: measuring accuracy

Authors: Abu Harera Nadeem, Kingsley Donkor

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Tea is a popular beverage due to its flavour, aroma and antioxidant properties—with the most consumed varieties being green and black tea. Antioxidants in tea can lower the risk of Alzheimer’s and heart disease and obesity. However, these teas contain heavy metals such as Hg, Cd, or Pb, which can cause autoimmune diseases like Graves disease. In this study, 11 heavy metals in various commercial green, black, and oolong tea samples were determined using inductively coupled plasma-mass spectrometry (ICP-MS). Two methods of sample preparation were compared for accuracy and precision, which were microwave-assisted digestion and ultrasonic-assisted extraction. The developed method was further validated by detection limit, precision, and accuracy. Results showed that the proposed method was highly sensitive with detection limits within parts-per-billion levels. Reasonable method accuracy was obtained by spiked experiments. The findings of this study can be used to delve into the link between tea consumption and disease and to provide information for future studies on metal determination in tea.

Keywords: ICP-MS, green tea, black tea, microwave-assisted acid digestion, ultrasound-assisted extraction

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Authors: Abe D. Desta

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This study suggests a deep learning-based method for identifying and following moving objects in surveillance video. The proposed method uses a fast regional convolution neural network (F-RCNN) trained on a substantial dataset of vehicle images to first detect vehicles. A Kalman filter and a data association technique based on a Hungarian algorithm are then used to monitor the observed vehicles throughout time. However, in general, F-RCNN algorithms have been shown to be effective in achieving high detection accuracy and robustness in this research study. For example, in one study The study has shown that the vehicle detection and tracking, the system was able to achieve an accuracy of 97.4%. In this study, the F-RCNN algorithm was compared to other popular object detection algorithms and was found to outperform them in terms of both detection accuracy and speed. The presented system, which has application potential in actual surveillance systems, shows the usefulness of deep learning approaches in vehicle detection and tracking.

Keywords: artificial intelligence, computer vision, deep learning, fast-regional convolutional neural networks, feature extraction, vehicle tracking

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Authors: Linghui Meng, James Atlas, Deborah Munro

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There is an increasing demand for prosthetics capable of mimicking natural limb movements and hand gestures, but precise movement control of prosthetics using only electrode signals continues to be challenging. This study considers the implementation of machine learning as a means of improving accuracy and presents an initial investigation into hand gesture recognition using models based on electromyographic (EMG) signals. EMG signals, which capture muscle activity, are used as inputs to machine learning algorithms to improve prosthetic control accuracy, functionality and adaptivity. Using logistic regression, a machine learning classifier, this study evaluates the accuracy of classifying two hand gestures from the publicly available Ninapro dataset using two-time series feature extraction algorithms: Time Series Feature Extraction (TSFE) and Convolutional Neural Networks (CNNs). Trials were conducted using varying numbers of EMG channels from one to eight to determine the impact of channel quantity on classification accuracy. The results suggest that although both algorithms can successfully distinguish between hand gesture EMG signals, CNNs outperform TSFE in extracting useful information for both accuracy and computational efficiency. In addition, although more channels of EMG signals provide more useful information, they also require more complex and computationally intensive feature extractors and consequently do not perform as well as lower numbers of channels. The findings also underscore the potential of machine learning techniques in developing more effective and adaptive prosthetic control systems.

Keywords: EMG, machine learning, prosthetic control, electromyographic prosthetics, hand gesture classification, CNN, computational neural networks, TSFE, time series feature extraction, channel count, logistic regression, ninapro, classifiers

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Authors: Mohammed Alquraish

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Saudi Arabia and other emerging nations have faced significant challenges in the sustainable construction industry. This paper presents a quantitative approach to assessing sustainability risk in the Saudi Arabian construction industry and offers insights into holistic sustainability design in industry operations. The implementation of sustainable construction industry practices in the manufacturing sector has been susceptible to several risk factors that need to be identified. In order to successfully execute sustainable building projects, decision makers in the fields of construction and industry can benefit greatly from the advice this study offers by promoting the elements that motivate sustainability implementation. Sustainability risk can be measured from combining failure probability with cumulative effects from sustainability factors: social, environmental, and economic; that affect the integrity of the construction industry. The cumulative effects of sustainability risk are measured by classifying the outcomes resulting from these consequences. Operators of industrial construction can strategically manage and minimize potential disruptions affecting long-term sustainability incentives by measuring sustainability risk. Thus, the suggested strategy greatly reinforces the crucial role of the construction industry.

Keywords: sustainability, risk, construction industry, Saudi Arabia

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Authors: Mostafa Kazemi, Zahra N. Farkhani

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This paper proposes an integrated Data Envelopment Analysis (DEA) and Variance Inflation Factor (VIF) model for measuring the technical efficiency of decision making units. The model is validated using a set of 69% sales representatives’ dairy products. The analysis is done in two stages, in the first stage, VIF technique is used to distinguish independent effective factors of resellers, and in the second stage we used DEA for measuring efficiency for both constant and variable return to scales status. Further DEA is used to examine the utilization of environmental factors on efficiency. Results of this paper indicated an average managerial efficiency of 83% in the whole sales representatives’ dairy products. In addition, technical and scale efficiency were counted 96% and 80% respectively. 38% of sales representative have the technical efficiency of 100% and 72% of the sales representative in terms of managerial efficiency are quite efficient.High levels of relative efficiency indicate a good condition for sales representative efficiency.

Keywords: data envelopment analysis (DEA), relative efficiency, sales representatives’ dairy products, variance inflation factor (VIF)

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Authors: Xin-Hua Zhao

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In recent years, the growth rate of social responsibility reports disclosed by Chinese corporations has grown rapidly. The economic effects of the growing corporate social responsibility reports have become a hot topic. The article takes the chemical listed engineering corporations that disclose social responsibility reports in China as a sample, and based on the information asymmetry theory, examines the economic effect generated by corporate social responsibility disclosure with the method of ordinary least squares. The research is conducted from the perspective of analysts’ earnings forecasts and studies the impact of corporate social responsibility information disclosure on improving the accuracy of analysts' earnings forecasts. The results show that there is a statistically significant negative correlation between corporate social responsibility disclosure index and analysts’ earnings forecast error. The conclusions confirm that enterprises can reduce the asymmetry of social and environmental information by disclosing social responsibility reports, and thus improve the accuracy of analysts’ earnings forecasts. It can promote the effective allocation of resources in the market.

Keywords: analysts' earnings forecasts, corporate social responsibility disclosure, economic effect, information asymmetry

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Authors: Fahd Sabry Esmail, M. Badr Senousy, Mohamed Ragaie

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In recent years, there has been an explosion in the rate of using technology that help discovering the diseases. For example, DNA microarrays allow us for the first time to obtain a "global" view of the cell. It has great potential to provide accurate medical diagnosis, to help in finding the right treatment and cure for many diseases. Various classification algorithms can be applied on such micro-array datasets to devise methods that can predict the occurrence of Leukemia disease. In this study, we compared the classification accuracy and response time among eleven decision tree methods and six rule classifier methods using five performance criteria. The experiment results show that the performance of Random Tree is producing better result. Also it takes lowest time to build model in tree classifier. The classification rules algorithms such as nearest- neighbor-like algorithm (NNge) is the best algorithm due to the high accuracy and it takes lowest time to build model in classification.

Keywords: data mining, classification techniques, decision tree, classification rule, leukemia diseases, microarray data

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Authors: Pavel Prochazka

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The study deals with transducers for measuring vibration displacements of rotating blade tips in turbomachines. In order to prevent major accidents with extensive economic consequences, it shows an urgent need for every low-pressure steam turbine stage being equipped with modern non-contact measuring system providing information on blade loading, damage and residual lifetime under operation. The requirement of measuring vibration and static characteristics of steam turbine blades, therefore, calls for the development and operational verification of both new types of sensors and measuring principles and methods. The task is really demanding: to measure displacements of blade tips with a resolution of the order of 10 μm by speeds up to 750 m/s, humidity 100% and temperatures up to 200 °C. While in gas turbines are used primarily capacitive and optical transducers, these transducers cannot be used in steam turbines. The reason is moisture vapor, droplets of condensing water and dirt, which disable the function of sensors. Therefore, the most feasible approach was to focus on research of electromagnetic sensors featuring promising characteristics for given blade materials in a steam environment. Following types of sensors have been developed and both experimentally and theoretically studied in the Institute of Thermodynamics, Academy of Sciences of the Czech Republic: eddy-current, Hall effect, inductive and magnetoresistive. Eddy-current transducers demand a small distance of 1 to 2 mm and change properties in the harsh environment of steam turbines. Hall effect sensors have relatively low sensitivity, high values of offset, drift, and especially noise. Induction sensors do not require any supply current and have a simple construction. The magnitude of the sensors output voltage is dependent on the velocity of the measured body and concurrently on the varying magnetic induction, and they cannot be used statically. Magnetoresistive sensors are formed by magnetoresistors arranged into a Wheatstone bridge. Supplying the sensor from a current source provides better linearity. The MR sensors can be used permanently for temperatures up to 200 °C at lower values of the supply current of about 1 mA. The frequency range of 0 to 300 kHz is by an order higher comparing to the Hall effect and induction sensors. The frequency band starts at zero frequency, which is very important because the sensors can be calibrated statically. The MR sensors feature high sensitivity and low noise. The symmetry of the bridge arrangement leads to a high common mode rejection ratio and suppressing disturbances, which is important, especially in industrial applications. The MR sensors feature high sensitivity, high common mode rejection ratio, and low noise, which is important, especially in industrial applications. Magnetoresistive transducers provide a range of excellent properties indicating their priority for displacement measurements of rotating blades in turbomachines.

Keywords: turbines, blade vibration, blade tip timing, non-contact sensors, magnetoresistive sensors

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Authors: Ines Zemni, Maher Slimane, Jamel Ben Hassouna, Khaled Rahal

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Background: Neurofibromatosis type 1 (NF1) is a genetic disease, which is associated with an increased risk of developing different malignancies including breast cancer. The association between NF1 band breast sarcoma is a rare entity. Herein we present a 25-year-old woman with NF1 who had fibrosarcoma of the left breast. Case presentation: The patient has multiple thoraco-abdominal 'café au lait' spots. Clinical examination showed a lump of the left breast measuring 9 cm of diameter, which was noticed for 6 months. There was a left inguinal mass of 6 cm of diameter. The patient underwent first a left lumpectomy. Histopathological exam revealed a high-grade fibrosarcoma of the left breast measuring 7.5 cm. Three months later, the patient underwent a left mastectomy and excision of the inguinal mass, which was a neurofibroma. An adjuvant chemotherapy and radiation therapy were indicated, but not applied because of the timeout. The patient is now alive after a follow up of 6 years, with no loco-regional recurrence or metastasis. Conclusion: The relationship between NF1 and breast cancer need to be more clarified by further studies. Establishing a specific screening program of these patients may help to make an earlier diagnosis of breast cancer.

Keywords: neurofibromatosis, breast, sarcoma, cancer

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Authors: Salah Ghamari

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In this study, the ability of self organizing map artificial (SOM) neural networks in clustering the wheat seeds varieties according to morphological properties of them was considered. The SOM is one type of unsupervised competitive learning. Experimentally, five morphological features of 300 seeds (including three varieties: gaskozhen, Md and sardari) were obtained using image processing technique. The results show that the artificial neural network has a good performance (90.33% accuracy) in classification of the wheat varieties despite of high similarity in them. The highest classification accuracy (100%) was achieved for sardari.

Keywords: artificial neural networks, clustering, self organizing map, wheat variety

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Authors: Güzi̇n Ti̇rkeş, Özge Teki̇n, Kerem Kurtuluş, Y. Yekta Yurtseven, Murat Baran

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A scanning electron microscope (SEM) is a type of electron microscope mainly used in nanoscience and nanotechnology areas. Automatic image recognition and classification are among the general areas of application concerning SEM. In line with these usages, the present paper proposes a deep learning algorithm that classifies SEM images into nine categories by means of an online application to simplify the process. The NFFA-EUROPE - 100% SEM data set, containing approximately 21,000 images, was used to train and test the algorithm at 80% and 20%, respectively. Validation was carried out using a separate data set obtained from the Middle East Technical University (METU) in Turkey. To increase the accuracy in the results, the Inception ResNet-V2 model was used in view of the Fine-Tuning approach. By using a confusion matrix, it was observed that the coated-surface category has a negative effect on the accuracy of the results since it contains other categories in the data set, thereby confusing the model when detecting category-specific patterns. For this reason, the coated-surface category was removed from the train data set, hence increasing accuracy by up to 96.5%.

Keywords: convolutional neural networks, deep learning, image classification, scanning electron microscope

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Authors: Xu Jiaqiao

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Text sentiment analysis is an important branch of natural language processing. This technology is widely used in public opinion analysis and web surfing recommendations. At present, the mainstream sentiment analysis methods include three parts: sentiment analysis based on a sentiment dictionary, based on traditional machine learning, and based on deep learning. This paper mainly analyzes and compares the advantages and disadvantages of the SVM method of traditional machine learning and the Long Short-term Memory (LSTM) method of deep learning in the field of Chinese sentiment analysis, using Chinese comments on Sina Microblog as the data set. Firstly, this paper classifies and adds labels to the original comment dataset obtained by the web crawler, and then uses Jieba word segmentation to classify the original dataset and remove stop words. After that, this paper extracts text feature vectors and builds document word vectors to facilitate the training of the model. Finally, SVM and LSTM models are trained respectively. After accuracy calculation, it can be obtained that the accuracy of the LSTM model is 85.80%, while the accuracy of SVM is 91.07%. But at the same time, LSTM operation only needs 2.57 seconds, SVM model needs 6.06 seconds. Therefore, this paper concludes that: compared with the SVM model, the LSTM model is worse in accuracy but faster in processing speed.

Keywords: sentiment analysis, support vector machine, long short-term memory, Chinese microblog comments

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Authors: Feng Guolin, Gong Zhiqiang

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In order to study the spatial structure and dynamical mechanism of extreme precipitation in East Asia, a corresponding climate network is constructed by employing the method of event synchronization. It is found that the area of East Asian summer extreme precipitation can be separated into two regions: one with high area weighted connectivity receiving heavy precipitation mostly during the active phase of the East Asian Summer Monsoon (EASM), and another one with low area weighted connectivity receiving heavy precipitation during both the active and the retreat phase of the EASM. Besides，a way for the prediction of extreme precipitation is also developed by constructing a directed climate networks. The simulation accuracy in East Asia is 58% with a 0-day lead, and the prediction accuracy is 21% and average 12% with a 1-day and an n-day (2≤n≤10) lead, respectively. Compare to the normal EASM year, the prediction accuracy is lower in a weak year and higher in a strong year, which is relevant to the differences in correlations and extreme precipitation rates in different EASM situations. Recognizing and identifying these effects is good for understanding and predicting extreme precipitation in East Asia.

Keywords: synchronization, climate network, prediction, rainfall

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Authors: Madhumita Sahu, S. S. Tiwary

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The Radiotherapy of Carcinoma Lung has always been difficult and a matter of great concern. The significant movement due to fractional motion caused due to non-rhythmic respiratory motion poses a great challenge for the treatment of Lung cancer using Ionizing Radiation. The present study compares the accuracy in the measurement of Target Volume using Slow-CT and 4DCT Imaging in SBRT for Lung Tumor. The experimental samples were extracted from patients with Lung Cancer who underwent SBRT. Slow-CT and 4DCT images were acquired under free breathing for each patient. PTV were delineated on Slow CT images. Similarly, ITV was also delineated on each of the 4DCT volumes. Volumetric and Statistical analysis were performed for each patient by measuring corresponding PTV and ITV volumes. The study showed (1) The Maximum Deviation observed between Slow-CT-based PTV and 4DCT imaging-based ITV is 248.58 cc. (2) The Minimum Deviation observed between Slow-CT-based PTV and 4DCT imaging-based ITV is 5.22 cc. (3) The Mean Deviation observed between Slow-CT-based PTV and 4DCT imaging-based ITV is 63.21 cc. The present study concludes that irradiated volume ITV with 4DCT is less as compared to the PTV with Slow-CT. A better and more precise treatment could be given more accurately with 4DCT Imaging by sparing 63.21 CC of mean body volume.

Keywords: CT imaging, 4DCT imaging, lung cancer, statistical analysis

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Authors: Akansha Tyagi, Mehar S. Sidhu, Ankur Mandal, Sanjay Kapoor, Sunil Dahiya, Jan M. Rost, Thomas Pfeifer, Kamal P. Singh

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An all-reflective split and delay unit is designed for dispersion free measurement of broadband ultrashort pulses using a pair of reflective knife edge prism for splitting and recombining of the measuring pulse. It is based on symmetrical wavefront splitting of the measuring pulse having two separate arms to independently shape both split parts. We have validated our delay line with NIR –femtosecond pulse measurement centered at 800 nm using second harmonic-Interferometric frequency resolved optical gating (SH-IFROG). The delay line is compact, easy to align and provides attosecond stability and precision and thus make it more versatile for wide range of applications in ultrafast measurements. We envision that the present delay line will find applications in IR-IR controlling for high harmonic generation (HHG) and attosecond IR-XUV pump-probe measurements with solids and gases providing attosecond resolution and wide delay range.

Keywords: HHG, nonlinear optics, pump-probe spectroscopy, ultrafast metrology

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Authors: Ching-Ting Hsu, Wei-Hua Ho, Yi-Chun Chang

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Video object tracking is one of the popular research topics in computer graphics area. The trajectory can be applied in security, traffic control, even the sports training. The trajectory for sports training can be utilized to analyze the athlete’s performance without traditional sensors. There are many relevant works which utilize mean shift algorithm with background subtraction. This kind of the schemes should select a kernel function which may affect the accuracy and performance. In this paper, we consider the motion information in the pre-coded bitstream. The proposed algorithm extracts the trajectory by composing the motion vectors from the pre-coded bitstream. We gather the motion vectors from the overlap area of the object and calculate mean of the overlapped motion vectors. We implement and simulate our proposed algorithm in H.264 video codec. The performance is better than relevant works and keeps the accuracy of the object trajectory. The experimental results show that the proposed trajectory extraction can extract trajectory form the pre-coded bitstream in high accuracy and achieve higher performance other relevant works.

Keywords: H.264, video bitstream, video object tracking, sports training

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Authors: Rodrigo Guimarães Santos

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The quality of public transport services has been considered as a critical factor by their users and also by users of individual transport. Thus, this dissertation aims to adapt a model that assesses the quality of public transport and determines its level of service based on the views of its users. The methodology is widely used by marketers and allows measuring the quality of services by assessing the perceptions and expectations of users. The adapted SERVQUAL was tested with users of public transport service users and car in Brasília-DF, city of Brazil. This research involved 241 questionnaires answered by people living in the various administrative regions of Brasília-DF. The analysis of the determinants pointed out that the quality of the public transport service offered in the city is low and users of public transport and cars have a high degree of expectations for improvement in all tested determinants. This method enabled the identification of the most critical determinants and those needing strategic actions for continuous improvement of quality. Adapting the SERVQUAL for a public transport service was satisfactory and demonstrated applicability to internal and external services, including measuring the public transport services in other cities with the opinion of the users.

Keywords: transportation services, quality services, servqual scale and marketing services

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Authors: Chanh Nghia Nguyen, Yu Kurokawa, Hirotsugu Inoue

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The surface roughness is an important parameter for evaluating the quality of material surfaces since it affects functions and performance of industrial components. Although stylus and optical techniques are commonly used for measuring the surface roughness, they are applicable only to accessible surfaces. In practice, surface roughness measurement from the back side is sometimes demanded, for example, in inspection of safety-critical parts such as inner surface of pipes. However, little attention has been paid to the measurement of back surface roughness so far. Since back surface is usually inaccessible by stylus or optical techniques, ultrasonic technique is one of the most effective among others. In this research, an ultrasonic pulse-echo technique is considered for evaluating the pitch and the height of back surface having periodic triangular profile as a very first step. The pitch of the surface profile is measured by applying the diffraction grating theory for oblique incidence; then the height is evaluated by numerical analysis based on the Kirchhoff theory for normal incidence. The validity of the proposed method was verified by both numerical simulation and experiment. It was confirmed that the pitch is accurately measured in most cases. The height was also evaluated with good accuracy when it is smaller than a half of the pitch because of the approximation in the Kirchhoff theory.

Keywords: back side, inaccessible surface, periodic roughness, pulse-echo technique, ultrasonic NDE

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Authors: Peter G. Hollis, Kim G. Clarke

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Accurate quantification of the overall volumetric oxygen transfer coefficient (KLa) is ubiquitously measured in bioprocesses by analysing the response of dissolved oxygen (DO) to a step change in the oxygen partial pressure in the sparge gas using a DO probe. Typically, the response lag (τ) of the probe has been ignored in the calculation of KLa when τ is less than the reciprocal KLa, failing which a constant τ has invariably been assumed. These conventions have now been reassessed in the context of multiphase bioprocesses, such as a hydrocarbon-based system. Here, significant variation of τ in response to changes in process conditions has been documented. Experiments were conducted in a 5 L baffled stirred tank bioreactor (New Brunswick) in a simulated hydrocarbon-based bioprocess comprising a C14-20 alkane-aqueous dispersion with suspended non-viable Saccharomyces cerevisiae solids. DO was measured with a polarographic DO probe fitted with a Teflon membrane (Mettler Toledo). The DO concentration response to a step change in the sparge gas oxygen partial pressure was recorded, from which KLa was calculated using a first order model (without incorporation of τ) and a second order model (incorporating τ). τ was determined as the time taken to reach 63.2% of the saturation DO after the probe was transferred from a nitrogen saturated vessel to an oxygen saturated bioreactor and is represented as the inverse of the probe constant (KP). The relative effects of the process parameters on KP were quantified using a central composite design with factor levels typical of hydrocarbon bioprocesses, namely 1-10 g/L yeast, 2-20 vol% alkane and 450-1000 rpm. A response surface was fitted to the empirical data, while ANOVA was used to determine the significance of the effects with a 95% confidence interval. KP varied with changes in the system parameters with the impact of solid loading statistically significant at the 95% confidence level. Increased solid loading reduced KP consistently, an effect which was magnified at high alkane concentrations, with a minimum KP of 0.024 s-1 observed at the highest solids loading of 10 g/L. This KP was 2.8 fold lower that the maximum of 0.0661 s-1 recorded at 1 g/L solids, demonstrating a substantial increase in τ from 15.1 s to 41.6 s as a result of differing process conditions. Importantly, exclusion of KP in the calculation of KLa was shown to under-predict KLa for all process conditions, with an error up to 50% at the highest KLa values. Accurate quantification of KLa, and therefore KP, has far-reaching impact on industrial bioprocesses to ensure these systems are not transport limited during scale-up and operation. This study has shown the incorporation of τ to be essential to ensure KLa measurement accuracy in multiphase bioprocesses. Moreover, since τ has been conclusively shown to vary significantly with process conditions, it has also been shown that it is essential for τ to be determined individually for each set of process conditions.

Keywords: effect of process conditions, measuring oxygen transfer coefficients, multiphase bioprocesses, oxygen probe response lag

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Authors: M. A. N. Masrom, M. H. I. A. Rahim, G. K. Chen, S. Mohamed

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Large infrastructure project is one of significant category in the development of Malaysian construction industry. This type of project has been recognized as a high complexity project with numerous construction risks, large cost involvement, highly technical requirements and divers of resources. Besides, the development of large infrastructure such as highway, railway, Mass Rapid Transit (MRT) and airport are also needed a large investment of public and private sector. To accomplish the development successfully, several challenges has to be determined prior the project commencement. To date, a comprehensive assessment of key success criteria particularly for large infrastructure in developing country such as Malaysia, is still not systematically defined and therefore, it needs further investigation. This paper aims to explore the potential success criteria that would be useful in gauging overall performance of large infrastructure implementation particularly in developing country. Previous successful criteria studies were used to develop a conceptual framework that possibly suitable for measuring large infrastructure performance. The findings show that successful criteria of infrastructure projects implementation could be grouped according to several key elements as it seems significant to the participants in prioritizing project challenges more systematically.

Keywords: successful criteria, performance, large infrastructure, Malaysia

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Authors: Frantisek Odvarka

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Based on the data from the Moravian Karst, the influence of the calcite speleothem growth by selected meteorological factors was evaluated. External temperature was determined as one of the main factors influencing speleothem growth in Moravian Karst. This factor significantly influences the CO₂ concentration in soil/epikarst, and cave atmosphere in the Moravian Karst and significantly contributes to the changes in the CO₂ partial pressure differences between soil/epikarst and cave atmosphere in Moravian Karst, which determines the drip water supersaturation with respect to the calcite and quantity of precipitated calcite in the Moravian Karst cave environment. External air temperatures and cave air temperatures were measured using a COMET S3120 data logger, which can measure temperatures in the range from -30 to +80 °C with an accuracy of ± 0.4 °C. CO₂ concentrations in the cave and soils were measured with a FT A600 CO₂H Ahlborn probe (value range 0 ppmv to 10,000 ppmv, accuracy 1 ppmv), which was connected to the data logger ALMEMO 2290-4, V5 Ahlborn. The soil temperature was measured with a FHA646E1 Ahlborn probe (temperature range -20 to 70 °C, accuracy ± 0.4 °C) connected to an ALMEMO 2290-4 V5 Ahlborn data logger. The airflow velocities into and out of the cave were monitored by a FVA395 TH4 Thermo anemometer (speed range from 0.05 to 2 m s⁻¹, accuracy ± 0.04 m s⁻¹), which was connected to the ALMEMO 2590-4 V5 Ahlborn data logger for recording. The flow was measured in the lower and upper entrance of the Imperial Cave. The data were analyzed in MS Office Excel 2019 and PHREEQC.

Keywords: speleothem growth, carbon dioxide partial pressure, Moravian Karst, external temperature

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Authors: Chung-Ming Lo, Kevin Li-Chun Hsieh

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The central nervous system in the World Health Organization defines grade 2, 3, 4 gliomas according to the aggressiveness. For brain tumors, using image examination would have a lower risk than biopsy. Besides, it is a challenge to extract relevant tissues from biopsy operation. Observing the whole tumor structure and composition can provide a more objective assessment. This study further proposed a computer-aided diagnosis (CAD) system based on a convolutional neural network to quantitatively evaluate a tumor's malignancy from brain magnetic resonance imaging. A total of 30 grade 2, 43 grade 3, and 57 grade 4 gliomas were collected in the experiment. Transferred parameters from AlexNet were fine-tuned to classify the target brain tumors and achieved an accuracy of 98% and an area under the receiver operating characteristics curve (Az) of 0.99. Without pre-trained features, only 61% of accuracy was obtained. The proposed convolutional neural network can accurately and efficiently classify grade 2, 3, and 4 gliomas. The promising accuracy can provide diagnostic suggestions to radiologists in the clinic.

Keywords: convolutional neural network, computer-aided diagnosis, glioblastoma, magnetic resonance imaging

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Authors: Waleed A. Basuliman, Khalid S. AlSaleh, Mohamed Z. Ramadan

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Applying the anthropometric dimensions is considered one of the important factors when designing any human-machine system. In this study, the estimation of anthropometric dimensions has been improved by developing artificial neural network that aims to predict the anthropometric measurements of the male in Saudi Arabia. A total of 1427 Saudi males from age 6 to 60 participated in measuring twenty anthropometric dimensions. These anthropometric measurements are important for designing the majority of work and life applications in Saudi Arabia. The data were collected during 8 months from different locations in Riyadh City. Five of these dimensions were used as predictors variables (inputs) of the model, and the remaining fifteen dimensions were set to be the measured variables (outcomes). The hidden layers have been varied during the structuring stage, and the best performance was achieved with the network structure 6-25-15. The results showed that the developed Neural Network model was significantly able to predict the body dimensions for the population of Saudi Arabia. The network mean absolute percentage error (MAPE) and the root mean squared error (RMSE) were found 0.0348 and 3.225 respectively. The accuracy of the developed neural network was evaluated by compare the predicted outcomes with a multiple regression model. The ANN model performed better and resulted excellent correlation coefficients between the predicted and actual dimensions.

Keywords: artificial neural network, anthropometric measurements, backpropagation, real anthropometric database

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Authors: E. Batista, R. Mendes, A. Furtado, M. C. Ferreira, I. Godinho, J. A. Sousa, M. Alvares, R. Martins

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Syringe pumps are commonly used for drug delivery in hospitals and clinical environments. These instruments are critical in neonatology and oncology, where any variation in the flow rate and drug dosing quantity can lead to severe incidents and even death of the patient. Therefore it is very important to determine the accuracy and precision of these devices using the suitable calibration methods. The Volume Laboratory of the Portuguese Institute for Quality (LVC/IPQ) uses two different methods to calibrate syringe pumps from 16 nL/min up to 20 mL/min. The Interferometric method uses an interferometer to monitor the distance travelled by a pusher block of the syringe pump in order to determine the flow rate. Therefore, knowing the internal diameter of the syringe with very high precision, the travelled distance, and the time needed for that travelled distance, it was possible to calculate the flow rate of the fluid inside the syringe and its uncertainty. As an alternative to the gravimetric and the interferometric method, a methodology based on the application of optical technology was also developed to measure flow rates. Mainly this method relies on measuring the increase of volume of a drop over time. The objective of this work is to compare the results of the calibration of two syringe pumps using the different methodologies described above. The obtained results were consistent for the three methods used. The uncertainties values were very similar for all the three methods, being higher for the optical drop method due to setup limitations.

Keywords: calibration, flow, interferometry, syringe pump, uncertainty

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Authors: Jie Xu, Zengshan Tian, Ze Li

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Previous indoor ranging or localization systems achieving high accuracy time of flight (ToF) estimation relied on two key points. One is to do strict time and frequency synchronization between the transmitter and receiver to eliminate equipment asynchronous errors such as carrier frequency offset (CFO), but this is difficult to achieve in a practical communication system. The other one is to extend the total bandwidth of the communication because the accuracy of ToF estimation is proportional to the bandwidth, and the larger the total bandwidth, the higher the accuracy of ToF estimation obtained. For example, ultra-wideband (UWB) technology is implemented based on this theory, but high precision ToF estimation is difficult to achieve in common WiFi or Bluetooth systems with lower bandwidth compared to UWB. Therefore, it is meaningful to study how to achieve high-precision ranging with lower bandwidth when the transmitter and receiver are asynchronous. To tackle the above problems, we propose a two-way channel error elimination theory and a frequency hopping-based carrier phase ranging algorithm to achieve high accuracy ranging under asynchronous conditions. The two-way channel error elimination theory uses the symmetry property of the two-way channel to solve the asynchronous phase error caused by the asynchronous transmitter and receiver, and we also study the effect of the two-way channel generation time difference on the phase according to the characteristics of different hardware devices. The frequency hopping-based carrier phase ranging algorithm uses frequency hopping to extend the equivalent bandwidth and incorporates a carrier phase ranging algorithm with multipath resolution to achieve a ranging accuracy comparable to that of UWB at 400 MHz bandwidth in the typical 80 MHz bandwidth of commercial WiFi. Finally, to verify the validity of the algorithm, we implement this theory using a software radio platform, and the actual experimental results show that the method proposed in this paper has a median ranging error of 5.4 cm in the 5 m range, 7 cm in the 10 m range, and 10.8 cm in the 20 m range for a total bandwidth of 80 MHz.

Keywords: frequency hopping, phase error elimination, carrier phase, ranging

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Authors: J. Carlos Kuri, Varun Vij, Raymond J. Turner, Orly Yadid-Pecht

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Celiac disease (CD) is an immune system disorder that is triggered by ingesting gluten. As a gluten-free (GF) diet has become a concern of many people for health reasons, a gold standard had to be nominated. Enzyme-linked immunosorbent assay (ELISA) has taken the seat of this role. However, multiple limitations were discovered, and with that, the desire for an alternative method now exists. Nucleic acid-based aptamers have become of great interest due to their selectivity, specificity, simplicity, and rapid-testing advantages. However, fluorescence-based aptasensors have been tagged as unstable, but lifespan details are rarely stated. In this work, the lifespan stability of a fluorescence-based aptasensor is shown over an 8-week-long study displaying the accuracy of the sensor and false negatives. This study follows 22 different samples, including GF and gluten-rich (GR) and soy sauce products, off-the-shelf products, and reference material from laboratories, giving a total of 836 tests. The analysis shows an accuracy of correctly classifying GF and GR products of 96.30% and 100%, respectively when the protocol is augmented with molecular sieves. The overall accuracy remains around 94% within the first four weeks and then decays to 63%.

Keywords: aptasensor, PEG, rGO, FAM, RM, ELISA

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Authors: Gehad S. Kaseb, Mona F. Ahmed

Abstract:

Arabic Sentiment Analysis (SA) is one of the most common research fields with many open areas. Few studies apply SA to Arabic dialects. This paper proposes different pre-processing steps and a modified methodology to improve the accuracy using normal Support Vector Machine (SVM) classification. The paper works on two datasets, Arabic Sentiment Tweets Dataset (ASTD) and Extended Arabic Tweets Sentiment Dataset (Extended-AATSD), which are publicly available for academic use. The results show that the classification accuracy approaches 86%.

Keywords: Arabic, classification, sentiment analysis, tweets

Procedia PDF Downloads 149

Authors: Indika Fernando, Jiangang Fei, Roger Stanely, Hossein Enshaei

Abstract:

Ever increasing demand for quality fresh produce by the consumers, had increased the gravity on the post-harvest supply chains in multi-fold in the recent years. Mechanical injury to fresh produce was a critical factor for produce wastage, especially with the expansion of supply chains, physically extending to thousands of miles. The impact of vibration damages in transit was identified as a specific area of focus which results in wastage of significant portion of the fresh produce, at times ranging from 10% to 40% in some countries. Several studies were concentrated on quantifying the impact of vibration to fresh produce, and it was a challenge to collect vibration impact data continuously due to the limitations in battery life or the memory capacity in the devices. Therefore, the study samples were limited to a stretch of the transit passage or a limited time of the journey. This may or may not give an accurate understanding of the vibration impacts encountered throughout the transit passage, which limits the accuracy of the results. Consequently, an approach which can extend the capacity and ability of determining vibration signals in the transit passage would contribute to accurately analyze the vibration damage along the post-harvest supply chain. A mechanism was developed to address this challenge, which is capable of measuring the in transit vibration continuously through the transit passage subject to a minimum acceleration threshold (0.1g). A system, consisting six tri-axel vibration transducers installed in different locations inside the cargo (produce) pallets in the truck, transmits vibration signals through LORA (Long Range Radio) technology to a central device installed inside the container. The central device processes and records the vibration signals transmitted by the portable transducers, along with the GPS location. This method enables to utilize power consumption for the portable transducers to maximize the capability of measuring the vibration impacts in the transit passage extending to days in the distribution process. The trial tests conducted using the approach reveals that it is a reliable method to measure and quantify the in transit vibrations along the supply chain. The GPS capability enables to identify the locations in the supply chain where the significant vibration impacts were encountered. This method contributes to determining the causes, susceptibility and intensity of vibration impact damages to fresh produce in the post-harvest supply chain. Extensively, the approach could be used to determine the vibration impacts not limiting to fresh produce, but for products in supply chains, which may extend from few hours to several days in transit.

Keywords: post-harvest, supply chain, wireless transducers, LORA, fresh produce

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Authors: Wisal Alrabadi

Abstract:

The study's goal was to see how the use of water wireless Aqua Com System and its accompanying music affected the Female Swimming Team at the Faculty of Physical Education's development of dolphin kick movements. To that end, a training program consisting of (12) training units spread out over four weeks, three units per week, was created and applied to a study sample of (10) students from the swimming pool enrolled in the first semester of the academic year 2022. Pre-measuring and timing the movements of dolphins kicking with and without fins above and below, measuring the water's surface over a distance of 25 meters. The results showed that there are statistically significant differences in favor of telemetry from the start within the limits of the area specified for a distance of 15 m after the comparison between the pre and post-measurement using the test (T) of the double samples, and this indicates the impact of the training program using the Aqua Com System in the swimming team(Female) at Faculty of Physical Education, and in light of this a set of recommendations was developed.

Keywords: aqua com system training program, accompanying music, dolphin kick movements, swimming team female

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Authors: Raúl Pereda García, Julio Manuel de Luis Ruiz, Elena Castillo López, Rubén Pérez Álvarez, Felipe Piña García

Abstract:

New technologies for the capture of point clouds have experienced a great advance in recent years. In this way, its use has been extended in geomatics, providing measurement solutions that have been popularized without there being, many times, a detailed study of its accuracy. This research focuses on the study of the viability of topographic works with drones incorporating different sensors sensitive to the visible spectrum. The fundamentals have been applied to a road, located in Cantabria (Spain), where a platform extension and the reform of a riprap were being constructed. A total of six flights were made during two months, all of them with GPS as part of the photogrammetric process, and the results were contrasted with those measured with total station. The obtained results show that the choice of the camera and the planning of the flight have an important impact on the accuracy. In fact, the representations with a level of detail corresponding to 1/1000 scale are admissible, depending on the existing vegetation, and obtaining better results in the area of the riprap. This set of techniques is, therefore, suitable for the control of earthworks in road works but with certain limitations which are exposed in this paper.

Keywords: drone, earth movement control, global position system, surveying technology.

Procedia PDF Downloads 184